The application of particle filters in geophysical systems is reviewed. Some background on
Bayesian filtering is provided, and the existing methods are discussed. The emphasis is on …

We investigate the properties of the hybrid Monte Carlo algorithm (HMC) in high dimensions.
HMC develops a Markov chain reversible with respect to a given target distribution Π using …

Through the development of an exact path integral for use in transferring information from
observations to a model of the observed system, the author provides a general framework …

The viewpoint taken in this paper is that data assimilation is fundamentally a statistical
problem and that this problem should be cast in a Bayesian framework. In the absence of …

Diffusion processes are a family of continuous-time continuous-state stochastic processes
that are in general only partially observed. The joint estimation of the forcing parameters and …

Data Assimilation in the Low Noise Regime with Application to the Kuroshio in: Monthly
Weather Review Volume 141 Issue 6 (2013) Jump to Content Jump to Main Navigation …

A novel distributed particle filter algorithm is presented, called drift homotopy likelihood
bridging particle filter (DHLB-PF). The DHLB-PF is designed to surmount the degeneracy …

This work introduces a Gaussian variational mean-field approximation for inference in
dynamical systems which can be modeled by ordinary stochastic differential equations. This …

This paper introduces a recursive particle filtering algorithm designed to filter high
dimensional systems with complicated non-linear and non-Gaussian effects. The method …

The process of transferring information from observations of a dynamical system to estimate
the fixed parameters and unobserved states of a system model can be formulated as the …